CN112230548B - Nuclear power device autonomous control system - Google Patents
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- CN112230548B CN112230548B CN202011162586.3A CN202011162586A CN112230548B CN 112230548 B CN112230548 B CN 112230548B CN 202011162586 A CN202011162586 A CN 202011162586A CN 112230548 B CN112230548 B CN 112230548B
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Abstract
The invention discloses an autonomous control system of a nuclear power plant, which comprises an organization planning layer, a task planning layer and a task scheduling layer, wherein the organization planning layer is used for conveying task instructions to an action coordination layer; the organization planning layer is also used for receiving the task state fed back by the action coordination layer and transmitting a task instruction to the action coordination layer according to the task state; the action coordination layer receives the task instruction and transmits the action instruction to the real-time execution layer according to the task instruction; the action coordination layer is also used for receiving the measurement parameters and the equipment state fed back by the real-time execution layer and transmitting an action instruction to the real-time execution layer according to the measurement parameters and the equipment state; when all the instructions of the task instructions are completely executed, the action coordination layer is also used for feeding the task state back to the organization planning layer; and the real-time execution layer drives the equipment to act according to the action command and feeds the measurement parameters and the equipment state back to the action coordination layer. The invention aims to provide an autonomous control system of a nuclear power device, which enables the nuclear power device control system to have higher automation and intelligence levels.
Description
Technical Field
The invention relates to the technical field of nuclear power device control, in particular to an autonomous control system of a nuclear power device.
Background
The autonomous Control is an intelligent Automatic Control (Automatic Control), and aims to realize the capability of autonomously selecting an optimal decision and continuously completing a mission of a system under uncertain objects and environments without depending on human intervention. The automatic control system accurately executes tasks according to a plan, and the autonomous control system can determine own behaviors according to decision knowledge of the autonomous control system to finish missions under the condition that the external and own conditions change, and has self-adaption, self-learning and self-decision capabilities. As a highly intelligent control theory, autonomous control has become a leading edge of research and a hotspot in the fields of mobile robots, unmanned aerial vehicles, intelligent vehicles, and the like.
The spacecraft needs to operate in space for a long time, and the flying orbit, attitude and the like of the spacecraft can only be autonomously controlled by a control system so as to deal with the operation control of the spacecraft in an uncertain environment and when the internal structure and parameters change. Therefore, the spacecraft can be said to be the most important and typical application of autonomous control, and the state of the art thereof is well representative.
When the spacecraft autonomous control system is designed, the execution level of tasks, fault diagnosis and reconstruction of the system, emergency treatment and the like need to be comprehensively considered, and meanwhile, the system also needs to have intelligence, reliability and globality. The system and structure are shown in fig. 1, the system adopts a three-layer control structure design, and comprises an organization planning layer, an action coordination layer and a real-time execution layer. The organization planning layer is involved in overall task planning, hierarchical management and intelligent decision-making; the action coordination layer obeys the management of the organization planning layer, is responsible for the distinguishing, analyzing and judging, logical reasoning and recording of system state characteristics, is responsible for coordinating and organizing various control strategies and is responsible for analyzing and processing various faults; the real-time execution layer is responsible for acquiring information and specifically executing various intelligent self-adaptive control strategies.
Reports similar to the autonomous control concept and system research of the nuclear power plant are not found in the national publicly developed table documents (only independent research on fault diagnosis, fault-tolerant control and the like is carried out).
Due to the potential radioactive hazard, the control system design of the nuclear power system is obviously conservative, and the existing devices in operation and construction still adopt the traditional control method which is widely verified and based on the classical control theory, but the advanced autonomous control concept is proposed as early as 20 world 90 s. However, currently, the autonomous control of the nuclear power system only provides a control scheme for an action coordination layer in a three-layer structure of the autonomous control system, does not provide a control scheme for an organization planning layer and a real-time execution layer, does not describe a specific interface relationship and a transmitted signal between the layers, and does not provide a relevant conclusion whether the organization planning layer and the real-time execution layer cancel or integrate.
In summary, at present, the related research on the autonomous control of the nuclear power plant is not developed in a targeted manner at home, the research on the control scheme is still in a scheme demonstration stage at abroad, the specific research is concentrated on an action coordination layer, and the research on the comprehensive coordination of the nuclear power plant is developed, but the related research results on how to design the autonomous control system of the nuclear power plant are lacked, and the complete description on the technical path of each level realization of the autonomous control system of the nuclear power plant is not provided.
Disclosure of Invention
The invention aims to provide an autonomous control system of a nuclear power device, which has higher automation and intelligence level, greatly reduces judgment decision work, psychological burden and human error probability of an operator during normal operation, and even independently executes a proper operation control strategy by human intervention, so that the nuclear power device is more suitable for application occasions with deeper and farther places, more complex environments and longer time.
The invention is realized by the following technical scheme:
an autonomous control system of a nuclear power plant comprises an organization planning layer, an action coordination layer and a real-time execution layer;
the organization planning layer is used for communicating task instructions to the action coordination layer; the organization planning layer is also used for receiving the task state fed back by the action coordination layer and transmitting a task instruction to the action coordination layer according to the task state;
the action coordination layer receives the task instruction and transmits a corresponding action instruction to the real-time execution layer according to the task instruction; the action coordination layer is also used for receiving the measurement parameters and the equipment state fed back by the real-time execution layer and transmitting action instructions to the real-time execution layer according to the measurement parameters and the equipment state; when all the instructions of the task instructions are completely executed, the action coordination layer is further used for feeding back the task state to the organization planning layer;
and the real-time execution layer drives equipment to act according to the action command and feeds back the measurement parameters and the equipment state to the action coordination layer.
Preferably, the organization planning layer is provided with a plurality of operating procedures of the reactor, and any one of the operating procedures comprises a plurality of tasks.
Preferably, any one of the tasks includes an entry condition, an operation content, and an exit condition;
the entry condition is used for judging whether the task can be executed or not;
the operation content is used for recording operation items of the task, and the operation items are arranged according to the sequence of the operation;
the exit condition is used for judging whether the task can be ended or not.
Preferably, the action coordination layer comprises a judgment module, an execution module and a first feedback module; the execution module comprises a sequential execution operation module, an intelligent optimization control module and a fault detection module;
the judging module is used for selecting a corresponding execution module to execute the task instruction according to the task instruction;
the sequence execution operation module is used for issuing corresponding action instructions to the control part of the real-time execution layer according to the task instructions;
the intelligent optimization control module is used for optimizing the initial control quantity and the measured parameters according to the task instruction to obtain an optimal solution of the control quantity and transmitting the optimal solution to the real-time execution layer;
the fault detection module is used for monitoring the state of the equipment in real time and judging whether the equipment has a fault or not; when the equipment fails, generating a fault-tolerant intervention instruction or calculating a fault-tolerant control mark through a fault-tolerant control strategy, and transmitting the fault-tolerant intervention instruction or the fault-tolerant control mark to the real-time execution layer;
the first feedback module is used for feeding back detection and execution information of tasks and fault results of equipment to the organization planning layer.
Preferably, the intelligent optimization control module comprises the following processing procedures:
acquiring a plurality of initial control quantities and actual measurement parameters;
sending a plurality of initial control quantities and the actually measured parameters into a prediction model, and calculating a plurality of predicted values;
selecting an optimal predicted value with the minimum deviation with an operation index from the plurality of predicted values, and acquiring a control quantity corresponding to the optimal predicted value;
and obtaining the optimal solution of the control quantity according to a particle swarm algorithm, and transmitting the optimal solution to the real-time execution layer.
Preferably, the predictive model is a T-S type fuzzy neural network.
Preferably, the real-time execution layer comprises a sequential control module, a closed-loop automatic control module, a fault-tolerant control module and a second feedback module;
the sequence control module is used for operating the equipment to perform corresponding actions according to the action instructions;
the closed-loop automatic control module is used for realizing closed-loop automatic tracking control according to the optimal solution of the controlled variable, the measured value of the controlled variable and the PID controller;
the fault-tolerant control module is used for operating an actuator to act according to the fault-tolerant intervention instruction or adjusting the parameters of the PID controller according to the fault-tolerant control mark;
and the second feedback module is used for feeding back the measurement parameters and the equipment state to the action coordination layer.
Preferably, the closed-loop automatic control module comprises a first adder, a PID controller, a second adder and a control object;
the first adder, the PID controller, the second adder and the control object are connected in sequence, and the control object is also connected with the first adder and the second adder; the PID controller is connected with the fault-tolerant control module, and the second adder is connected with the intelligent optimization control module.
Preferably, the organization planning layer can also be used for directly controlling the equipment to perform corresponding actions.
Preferably, the organization planning layer is further configured to monitor the operation parameters and the device status of the autonomous control system in real time, and combine the operation parameters and the device status with the device fault detection result fed back by the action coordination layer to jointly determine whether a new event occurs.
Compared with the prior art, the invention has the following advantages and beneficial effects:
(1) the real-time execution layers are distributed in different control loops, each control loop only pays attention to the information of the loop, the field measurement parameters are directly adopted to participate in control calculation, and the input and output of the control loop directly correspond to a field measuring point and a field control part, so that the control process is more accurate;
(2) the real-time execution layer adopts a simple and reliable controller, utilizes cascade-feedforward loop control to send the deviation between a set value and a feedback measured value into the controller, calculates the quantity with a small amount of data, outputs a control signal in real time, responds to the change of a measurement signal by quickly and frequently changing the control signal, and generates a control action of eliminating the deviation, so that the measured value tracks the set value;
(3) the action coordination layer occupies a plurality of control periods, an intelligent optimization algorithm with large calculation data volume, complex algorithm and multiple iteration steps is adopted, the control set value of operation optimization is calculated in a non-real-time manner, a large amount of information is fully utilized to carry out global operation optimization, and the optimization result is more accurate.
Drawings
The accompanying drawings, which are included to provide a further understanding of the embodiments of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the principles of the invention. In the drawings:
FIG. 1 is a schematic structural diagram of an exemplary autonomous control system for a spacecraft of the present invention;
FIG. 2 is a schematic block diagram of the system of the present invention;
FIG. 3 is a schematic diagram of an intelligent optimization control module according to the present invention;
fig. 4 is a schematic structural diagram of a fault detection module according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to examples and accompanying drawings, and the exemplary embodiments and descriptions thereof are only used for explaining the present invention and are not meant to limit the present invention.
A nuclear power plant autonomous control system, as shown in fig. 2, includes an organization planning layer, an action coordination layer, and a real-time execution layer.
The organization planning layer is a top entry of each function and is responsible for executing the operation rules of the reactor. The operation procedure takes tasks as carriers, and each task comprises an entrance condition, an exit condition, operation content and the like.
When the system works, the organization planning layer judges the skipping of the task according to the running information of the autonomous control system and the task feedback of the action coordination layer, and issues the specific task to be executed to the action coordination layer. In addition, in special cases and during ground operation of the reactor, the organization planning layer can directly operate specific control positions.
In this embodiment, all the operation procedures and tasks in the organization planning layer are connected in order to realize the reactor condition conversion and the event handling, and the operation procedures and tasks are event-triggered without a fixed control period. The organization planning layer contains all expected events and their treatment protocols, performs a predetermined limited number of tasks and defines the correct ones.
Specifically, in this embodiment, the general targets, sub-targets, and tasks of each operating procedure of the nuclear power plant are designed and decomposed in advance, the conditions and constraints of each task are determined, and the task sequence formed according to the flow includes a plurality of branches formed by entry conditions, work contents, exit conditions, and jump relationships. Taking a pressurized water reactor as an example, if the reactor is put into operation, the reactor is started first, and the specific tasks of the reactor are water filling, gas expelling, critical reaching, temperature rising and pressure rising … …; the inlet condition of the water filling task is that each device is checked to be normal, the constraint condition of the primary circuit pressure is met when water filling is executed, the operation content is water filling and air removing, and the outlet condition is that the next step is carried out until water overflows. The entry conditions and the exit conditions of the task sequence are comprehensively judged according to the detection and execution information of the specific tasks fed back by the action coordination layer and by combining the overall operation parameters of the nuclear power plant, and the judgment result can continue to execute the subsequent tasks, and can jump to other tasks or even accident tasks.
It is worth to be noted that the organization planning layer can also be developed in a graphical manner through a security state machine provided by a security critical software development environment (SCADE), and static simulation verification is completed under the SCADE environment, so that execution of each task is ensured to be consistent with the overall targets and sub-targets of the operating rules.
The action coordination layer is an entrance of a specific task of the organization planning layer, receives a task instruction issued by the organization planning layer, and selects an execution module for executing the current task instruction by using a rule in a knowledge base (a judgment module). The rule described in this embodiment is a combination of a series of preset conditions and corresponding actions, and when the conditions are satisfied, the corresponding actions are executed. The current task is a macroscopic instruction, specific operation is not indicated, the autonomous control system judges what specific operation should be executed at present according to rules, if the task is power-up, sequential operation for load lifting is executed firstly, after the deviation of the load and the nuclear power and the temperature deviation of the reactor reach threshold values, the reactor tracks the load through the intelligent optimization control module, fault detection is carried out simultaneously in the process, and when the condition of relevant fault rules is met, fault handling operation is executed.
In addition, the execution module in this embodiment includes a sequential execution operation module, an intelligent optimization control module, a fault detection module, and a first feedback module.
And the sequential execution operation module is used for issuing an operation instruction to a specific control part of the real-time execution layer.
It is worth to be noted that the sequential execution operation module can also be developed in a graphical manner through a state machine provided by a safety critical software development environment (SCADE), and static simulation verification is completed under the SCADE environment, so that execution of each operation is ensured to be consistent with an operation rule.
As shown in fig. 3, the intelligent optimization control module solves the global operation optimization problem by using the output value of the prediction model and the intelligent optimization algorithm to obtain the optimal solution of the control action, and transmits the optimal solution to the real-time execution layer as the set value of the control loop, thereby implementing global adaptive control to cope with the characteristic changes of the controlled object including the fault.
Specifically, the intelligent optimization control module uses a T-S type fuzzy neural network as a prediction model, and the input vector thereof uses the measured values of the relevant parameters in the current and past periods of time, and outputs the predicted values as the predicted values of the relevant parameters at the next time, for example, the predicted values at the next time of the reactor average temperature and the nuclear power are respectively calculated by using the measured values of the reactor average temperature, the nuclear power, the feedwater flow and the steam flow in the current and past 2 sampling periods. The off-line training of the T-S type fuzzy neural network can determine the regular number of the T-S fuzzy neural network through fuzzy clustering by a large amount of data accumulated by a simulation test, and the parameters of the T-S fuzzy neural network are learned through a Kalman filter. Under the online condition that the control system is put into real-time operation, the Kalman filter is utilized to learn only the parameters of the rule closest to the Euclidean distance of the current input vector of the T-S fuzzy neural network, and the learning input is the error between the predicted value and the current measured value, so that the prediction model can approach the actual measurement output.
The deviation of the set value and the measured value of the relevant parameter is multiplied by the weighting coefficient, the deviation of the demand load and the nuclear power is also multiplied by the weighting coefficient, the deviation and the nuclear power are added to be used as an optimization target function, the stroke, the speed range, the allowable fluctuation range of the relevant parameter and the like of the relevant actuator are used as constraint conditions, the particle swarm algorithm is used for iteratively searching for the optimal optimization set value, and the target function is enabled to take the minimum value. The objective function may be a deviation of the reactor mean temperature from a reference temperature, a deviation of the steam generator water level from a reference water level, a deviation of the nuclear power from the demand load, a deviation of the feedwater flow from the demand load, and the like.
During normal operation, the disturbance, uncertainty influence and the change of self-operation characteristics (such as fuel consumption, coolant flow, loop pressure and heat transfer capacity waiting) of the nuclear power device are identified by a prediction model through online learning, and a corresponding optimization set value is generated by an intelligent optimization algorithm.
The fault detection module, as shown in fig. 4, includes confirmation of a fault, isolation of related signals, devices, and the like, redundancy switching, generation of a fault-tolerant reconfiguration control flag, and the like. Meanwhile, the fault detection module is also used for issuing the fault-tolerant reconstruction control mark to a real-time execution layer.
Specifically, in this embodiment, the fault detection module establishes a Principal Component Analysis (PCA) estimation model of the sensor for the relevant measurement signal by using a PCA method and simulation data, performs Q-statistical analysis (Q-statistical) on a residual between an estimated value and an actual measurement value of the sensor, selects a statistical index and determines a statistical threshold of the statistical index in the normal signal, performs Q-statistical analysis on the residual between the PCA estimated value and the actual measurement value under an online condition, calculates the statistical index, and determines the signal fault if the statistical index exceeds the statistical threshold of the normal signal.
The electric control driving system of the actuator has the fault of determining the fault mode, the fault detection is carried out according to the state signals of relevant action, electricity and the like by the electric control driving system according to the expert rule, for example, the rod control position system judges the step loss, the failure, the misoperation and the like of the fault of the reactor control rod driving mechanism according to the deviation of the measured rod position and the given rod position, and the pump valve is the same. The actuator does not clear faults of fault modes, such as performance degradation, insufficient action amplitude, excessive action and the like, can be regarded as changes of characteristics of the control object, and the changes are identified through online learning by the prediction model.
The fault detection of the digital controller is realized by the digital controller.
In the operation control of the nuclear power device, after a sensor and an actuator are removed, a nuclear power device control object has a fault of a determined mode, fault detection is carried out according to relevant system operation parameters and equipment states and expert rules, the fault of the nuclear power device without the determined fault mode can be regarded as the change of the characteristics of the control object, and the fault is identified by a prediction model through online learning. The expert rules are concepts known in the art, and refer to judging according to preset rules and executing corresponding operations.
It should be noted that the fault-tolerant control strategy of the present application is: if the sensors fail, the sensor signals are isolated, the measurement signals participating in control calculation are screened out from the remaining multi-path measurement signals, if all the sensors fail, the predicted values of the prediction model are used for participating in control calculation, and the actual measurement signals of the faults are not adopted any more. If the digital controller fails, the digital controller realizes master-slave switching between the redundancy function modules. When the fault with the determined fault mode occurs in the actuator or the nuclear power device, the corresponding fault handling sequence control flow is switched in according to the judgment result of the expert rule, if the actuator is mistakenly operated and refused to operate for more than the preset time, the standby actuator is switched to operate, the steam turbine is in fault load shedding, and the fast plunger reduces the nuclear power, the water supply flow and the like.
And when the accident is possibly caused by partial fault of the nuclear power device, the first feedback module of the action coordination layer feeds back fault diagnosis information to the organization planning layer, and the fault diagnosis information is subjected to a corresponding accident task after being subjected to criterion.
Faults of the actuator or the nuclear power device, which do not determine a fault mode, are identified through online learning by the prediction model, and corresponding optimized set values are generated by the intelligent optimization algorithm, so that the control performance after the faults is still guaranteed as far as possible in the sense of minimizing an optimized objective function. If the relevant parameters or indexes do not reach the expected fault-tolerant value within a certain time after the optimized set value is adjusted, a fault-tolerant control mark is generated, and the mark enables the parameters of the PID controller of the real-time execution layer to change according to the direction of reducing the deviation of the corresponding control variable.
The real-time execution layer is used for organizing direct operation instructions of the planning layer, sequential operation instructions of the action coordination layer, an optimized set value of the control loop and an entrance of a fault-tolerant reconstruction setting mark. After the real-time executive layer receives the signals, or the equipment of the specific control part is operated; or the received optimized set value is compared with the corresponding measured value, and the closed-loop automatic control of each loop is realized by utilizing the deviation and the PID controller and adopting a cascade-feedforward structure; or the received fault-tolerant reconstruction control mark is adopted to adjust the parameters of the PID controller. Meanwhile, each loop of the layer also feeds back relevant measurement parameters, equipment states and the like to the action coordination layer to participate in control decision.
Specifically, when the real-time execution layer receives the operation instruction issued by the action coordination layer, the sequence control module operates the equipment at the specific control part according to the operation instruction issued by the action coordination layer, such as starting, stopping, inching, continuous adjustment and the like.
When the real-time execution layer receives the optimized set value transmitted by the action coordination layer, the closed-loop automatic control module compares the optimized set value with the corresponding measured value, and then realizes the closed-loop automatic control of each loop by using the deviation of the measured value and the optimized set value and the PID controller. As shown in fig. 1, the closed-loop automatic control module includes a first adder, a PID controller, a second adder and a control object, wherein the first adder, the PID controller, the second adder and the control object are connected in sequence, and the control object is further connected to the first adder and the second adder; and the PID controller is connected with the fault-tolerant control module, and the second adder is connected with the intelligent optimization control module.
When the intelligent optimization control system works, the first adder subtracts the actual measurement parameter value from the initial value, then the initial value is sent to the PID controller to generate a basic control quantity, the second adder adds the basic control quantity with the optimization setting value generated by the intelligent optimization control module and subtracts the actual measurement parameter to obtain the basic control quantity, and then the basic control quantity and the optimization setting value act on a control object to enable the actual measurement parameter of the control object to change. Meanwhile, the fault-tolerant control module selects a corresponding PID parameter adjusting mode according to the judgment detection result, and changes the PID parameters of the PID controller.
When the real-time execution layer receives the fault-tolerant intervention instruction or the fault-tolerant control mark transmitted by the action coordination layer, the fault-tolerant control module operates the action of the actuator according to the fault-tolerant intervention instruction or adjusts the parameters of the PID controller according to the fault-tolerant control mark. For example, the proportional gain of the reactor average temperature PID controller may be multiplied by 2 times the absolute value of the deviation between the reactor average temperature and the reference temperature.
And the second feedback module is used for feeding back relevant measurement parameters, equipment states and the like of all loops of the real-time execution layer to the action coordination layer to participate in control decision.
The measurement parameters and the equipment state referred to in this embodiment refer to parameters and states that are pre-specified and collected in an autonomous control system scheme, and are different according to different specific reactor control objects, such as reactor temperature, coolant flow, start/stop states of an electric heater of a voltage stabilizer, power states of control equipment, and the like in a pressurized water reactor. The parameters are acquired by a measuring system instrument and a self-contained state detection module of the equipment, and the signals can be directly acquired for use after the autonomous control system is connected with the autonomous control system.
The present invention will be further described with reference to specific examples.
The operation of the autonomous control system is started from the receipt of a reactor start command, and whether the conditions of a reactor start task are met or not is judged; if the conditions of the stack starting task are met, judging whether the conditions of entering each subtask are met; if the conditions for entering each subtask are met, a task instruction is sent to an action coordination layer; the action coordination layer mainly executes sequential operations, generates operations according to corresponding rule conditions and issues action instructions to the real-time execution layer; the real-time execution layer drives equipment to act, and feeds back measurement parameters, equipment states and the like to the action coordination layer; the action coordination layer judges according to the feedback according to the operation rules, continues the next operation if the conditions are met, otherwise enters other operation branches, and feeds the task state back to the organization planning layer after all the operations of the current task are executed; and after confirming that the task exit condition is met, the organization planning layer carries out the next task until the stack starting task is completed.
After the heap is started, the organization planning layer carries out a power operation task and also issues the task to the action coordination layer according to the mode; the action coordination layer executes intelligent operation optimization operation at the moment; sending a large amount of alternative initial control quantities and actual measurement parameters into a prediction model to calculate a large amount of predicted values, selecting a control quantity corresponding to an optimal predicted value with minimum deviation with an operation index as an intermediate solution, iterating multiple steps by using an intelligent optimization algorithm until the optimal solution of the control quantity is searched, and then sending the optimal solution to a real-time execution layer as a set value of a closed-loop control loop of the real-time execution layer; the real-time execution layer utilizes the set value to participate in the closed-loop control operation of the real-time execution layer, finally generates the control quantity of the corresponding equipment, and simultaneously feeds back the measurement parameters, the equipment state and the like to the action coordination layer to participate in the calculation of intelligent operation optimization.
In addition, starting from the operation of the input autonomous control system, a fault detection module of the action coordination layer continuously monitors the state of related equipment, and whether a fault occurs is judged through an online fault detection algorithm; if the fault occurs, generating a fault-tolerant intervention instruction, or calculating a fault-tolerant control mark through a fault-tolerant control strategy and sending the fault-tolerant control mark to a real-time execution layer; the real-time execution layer operates the actuator to act according to the fault-tolerant intervention instruction or adjusts the parameters of the controller according to the fault-tolerant control mark, so that the fault-tolerant control action is implemented, and related parameters and states are fed back to the action coordination layer.
On the other hand, from the beginning of the operation of the input autonomous control system, the organization planning layer continuously monitors the overall operation parameters and states of the system, and combines the overall operation parameters and states with the equipment fault detection result fed back by the action coordination layer to jointly judge whether a new event occurs, so that tasks of the corresponding event, such as lifting power and post-treatment of various expected accidents (except triggering protection actions), are entered according to the operation rules.
The new event mentioned in this embodiment means that the change of the operating parameters and the equipment state is significant enough to reach the entry condition of another rule, for example, during the execution of the normal power operating rule, when the reactor temperature is significantly increased to reach the fixed value requiring shutdown protection, events such as overpower and coolant loss may occur, and after the specific event is judged by the parameters and the state, the corresponding protection rule should be entered.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are merely exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (9)
1. An autonomous control system of a nuclear power plant is characterized by comprising an organization planning layer, an action coordination layer and a real-time execution layer;
the organization planning layer is used for communicating task instructions to the action coordination layer; the organization planning layer is also used for receiving the task state fed back by the action coordination layer and transmitting a task instruction to the action coordination layer according to the task state;
the action coordination layer receives the task instruction and transmits a corresponding action instruction to the real-time execution layer according to the task instruction; the action coordination layer is also used for receiving the measurement parameters and the equipment state fed back by the real-time execution layer and transmitting action instructions to the real-time execution layer according to the measurement parameters and the equipment state; when all the instructions of the task instructions are completely executed, the action coordination layer is further used for feeding back the task state to the organization planning layer;
the real-time execution layer drives equipment to act according to the action command and feeds back the measurement parameters and the equipment state to the action coordination layer;
the action coordination layer comprises an intelligent optimization control module, and the intelligent optimization control module is used for optimizing the initial control quantity and the measured parameters according to the task instruction to obtain an optimal solution of the control quantity and transmitting the optimal solution to the real-time execution layer;
the intelligent optimization control module comprises the following processing procedures:
acquiring a plurality of initial control quantities and a plurality of measured control quantities;
sending the plurality of initial control quantities and the plurality of actually measured control quantities into a prediction model, and calculating a plurality of predicted values;
selecting an optimal predicted value with the minimum deviation with an operation index from the plurality of predicted values, and acquiring a control quantity corresponding to the optimal predicted value;
and acquiring the optimal solution of the controlled variable according to a preset objective function, constraint conditions and a particle swarm algorithm, and transmitting the optimal solution of the controlled variable to the real-time execution layer.
2. The system of claim 1, wherein the organization planning layer is configured with a plurality of operational protocols for the reactor, any one of the operational protocols including a plurality of tasks.
3. The nuclear power plant autonomous control system of claim 2, wherein any one of the tasks includes an entry condition, an operation content, and an exit condition;
the entry condition is used for judging whether the task can be executed or not;
the operation content is used for recording operation items of the task, and the operation items are arranged according to the sequence of the operation;
the exit condition is used for judging whether the task can be ended or not.
4. The nuclear power plant autonomous control system of any one of claims 1-3, wherein the action coordination layer comprises a determination module, a first feedback module, a sequential execution operation module, and a fault detection module;
the judging module is used for selecting a corresponding execution module to execute the task instruction according to the task instruction;
the sequence execution operation module is used for issuing corresponding action instructions to the control part of the real-time execution layer according to the task instructions;
the fault detection module is used for monitoring the state of the equipment in real time and judging whether the equipment has a fault or not; when the equipment fails, generating a fault-tolerant intervention instruction or calculating a fault-tolerant control mark through a fault-tolerant control strategy, and transmitting the fault-tolerant intervention instruction or the fault-tolerant control mark to the real-time execution layer;
the first feedback module is used for feeding back detection and execution information of tasks and fault results of equipment to the organization planning layer.
5. The nuclear power plant autonomous control system of claim 4, wherein the predictive model is a T-S type fuzzy neural network.
6. The system according to claim 5, wherein the real-time execution layer comprises a sequence control module, a closed-loop automatic control module, a fault-tolerant control module and a second feedback module;
the sequence control module is used for operating the equipment to perform corresponding actions according to the action instructions;
the closed-loop automatic control module is used for realizing closed-loop automatic tracking control according to the optimal solution of the control quantity, the actually measured control quantity and the PID controller;
the fault-tolerant control module is used for operating an actuator to act according to the fault-tolerant intervention instruction or adjusting the parameters of the PID controller according to the fault-tolerant control mark;
and the second feedback module is used for feeding back the measurement parameters and the equipment state to the action coordination layer.
7. The nuclear power plant autonomous control system of claim 6, wherein the closed-loop automatic control module comprises a first summer, a PID controller, a second summer, and a control target;
when the device works, the first adder subtracts the actually measured control quantity from the initial control quantity, then the initial control quantity is sent to the PID controller to generate a basic control quantity, and the second adder adds the basic control quantity to the optimal solution of the control quantity and subtracts the actually measured parameter value to act on a control object, so that the actually measured parameter of the control object changes.
8. The system of claim 7, wherein the tissue planning layer is further operable to directly control the device to perform the corresponding actions.
9. The system of claim 8, wherein the organization planning layer is further configured to monitor the operating parameters and the device status of the autonomous control system in real time, and determine whether a new event occurs in combination with the device failure detection result fed back by the action coordination layer.
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